Even though there are multiple methods for discovering causal structures, recent methods with higher computationally efficiency and statistical accuracy focused on differentiable structure learning are a promising direction. Generally, these methods define a structural causal model with differentiable parameters that are adjusted to fit the data. In this work, we mainly use ENCO [1]}, which presents an efficient neural structure discovery by reformulating the graph search as optimization independent edge likelihoods and also conduct some experiments with SDI [2]}, that proposes learning of the adjacency matrix of the data generation process as a matrix parameter of a neural network.
